Towards an Interpretable AI Framework for Advanced Classification of Unmanned Aerial Vehicles (UAVs) | |
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Author | |
Abstract |
With UAVs on the rise, accurate detection and identification are crucial. Traditional unmanned aerial vehicle (UAV) identification systems involve opaque decision-making, restricting their usability. This research introduces an RF-based Deep Learning (DL) framework for drone recognition and identification. We use cutting-edge eXplainable Artificial Intelligence (XAI) tools, SHapley Additive Explanations (SHAP), and Local Interpretable Model-agnostic Explanations(LIME). Our deep learning model uses these methods for accurate, transparent, and interpretable airspace security. With 84.59\% accuracy, our deep-learning algorithms detect drone signals from RF noise. Most crucially, SHAP and LIME improve UAV detection. Detailed explanations show the model s identification decision-making process. This transparency and interpretability set our system apart. The accurate, transparent, and user-trustworthy model improves airspace security. |
Year of Publication |
2024
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Date Published |
jan
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URL |
https://ieeexplore.ieee.org/document/10454862
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DOI |
10.1109/CCNC51664.2024.10454862
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Google Scholar | BibTeX | DOI |